We demonstrate that RL\(^3\)  captures many aspects of human exploration by reanalyzing data from three previously conducted psychological studies. First, we show that it explains human choices in a two-armed bandit task on both a qualitative and quantitative level. In this setting, RL\(^3\)  discovers a set of diverse exploration strategies, allowing us to account for individual differences in human decision-making. It furthermore provides a better fit to human data than traditional approaches, such as Thompson sampling [1]}, upper confidence bound (UCB) algorithms [2]}, and mixtures thereof [3]}. We then verify that the manipulation of computational resources in this class of models matches the manipulation of resources in human subjects in two different contexts: (1) Lowering the description length of RL\(^3\)  leads to decision-making defects that have been reported in patients with brain lesions. (2) Increasing the description length of RL\(^3\)  aligns with the trajectory of exploration strategies that children go through during their cognitive development. Taken together, these results enrich our understanding of human exploration and provide insights into how to improve the exploratory capabilities of artificial agents.
